X

My best guess is that for scaled RL most of the top Chinese AI labs are starting to use a lot of Huawei for inference and Nvidia for training (maybe not for weird architectures). As agent swarms, even more scaled post-training, etc becomes the norm, this will accelerate their domestic industry.

https://t.co/mBcpoi9wrw

A few (personal) thoughts on reading empirical AI papers on the economy. Economists have gotten used to reading papers with super clean identification, arguing about the validity of an instrument, making sure parallel trend assumptions are satisfied. This is what gets you into a top journal, and it is *very* important research (no question here). But it also takes years and sometimes decades to get these types of papers right---people often don't find a good instrument to answer a specific causal question decades after the natural experiment. We will eventually have this type of research for AI as well, and it is absolutely necessary. But right we also need signals *right now*, even if they are noisier than what we are used to. We need papers where we can trust that researchers did their best methodologically, while at the same time acknowledging that the space is moving way too fast to wait for perfect identification. This will allow us to accumulate enough signals, coming at the same question using different angles, for example, to say "yes, X is likely happening in the economy". The AI exposure and early career hiring papers are a good example of this. There is no silver bullet paper with super clean identification. But at this point we have several independent teams reaching the same general conclusion, enough where we can say "there seems to be a slow down in AI-exposed, early career hiring."

Tesla helped save a man from jail. Kevin Finley was arrested at gunpoint for allegedly fleeing police. His Tesla captured the key moments, showing why he never saw the unmarked police car. Nearly two years later, the judge watched the video and found him not guilty. https://t.co/BN4WeFWkqd